In this series, we embark on a speculative journey back in time to reimagine the trajectory of Kodak, a titan of the photography industry faced with the seismic shift from analog to digital. With each blog post, we delve into distinct stages of applying modern pragmatic innovation and data science techniques to Kodak’s historical context. Our exploration is not just a reflection on what could have been, but more of a discussion of insights, strategies, and methodologies designed for today’s businesses navigating their own digital transformations.

At each step of this series, we scrutinize pivotal moments and decisions through the lens of contemporary data science and innovation practices. From identifying missed opportunities for market recovery to deploying cutting-edge models, our narrative serves as both a speculative examination and a practical guide, underscoring the profound impact that advanced data analytics, intelligent orchestration, and strategic deployment could have on reshaping business outcomes.

This journey, while rooted in Kodak’s past, is rich with lessons for the present and future. It reminds us that the tools and techniques of pragmatic innovation offer not just a path to overcoming challenges but also a bridge to seizing unprecedented opportunities. Join us as we continue to unravel this speculative yet instructive saga, drawing valuable lessons for businesses striving to innovate and thrive in an ever-changing digital landscape.

The Kodak Moment That Wasn't: Framing Pragmatic Innovation with Data Science and AI

Imagine a world where Kodak, the once-ubiquitous name in photography, evolved to become a leader in digital imaging. This isn't a fantasy but a missed reality that offers a profound lesson in the importance of pragmatic innovation. As we navigate the complexities of the digital age, Kodak's journey from pioneering the digital camera to filing for bankruptcy serves as a cautionary tale. It underscores the critical need for businesses to embrace change, leverage data science, and employ artificial intelligence (AI) in transforming their strategies for the future.

In the 2021 article “Beyond the Hype: Why Do Data-Driven Projects Fail,” the authors state the main reasons for failure in data-driven projects are (1) the lack of understanding of the business context and user needs, (2) low data quality, and (3) data access problems. The team surveyed 112 experts with experience in data projects from eleven industries and noted that 54% of respondents see a conceptual gap between business strategies and the implementation of analytics solutions. Pragmatic innovation by nature is intended to always be closely aligned with the business; it helps bridge gaps between business problems and technical solutions. Pragmatic innovation is not just about adopting new technologies but about systematically solving business problems with a strategic focus.

Data science and AI offer powerful tools for identifying trends, predicting market shifts, and understanding customer needs. For Kodak, applying these technologies could have meant capturing market trends before they developed, engaging customers with personalized experiences, and innovating products to stay ahead of the competition.

The Missed Shot: Kodak and Digital Photography

In 1975, Kodak invented the digital camera, a breakthrough that could have redefined the industry. Can you imagine if they had pushed forward at that time? Who knows where we’d be now…20 years in the future? Instead, the company hesitated, prioritizing its film-based business model despite clear signs of a digital wave on the horizon. This pivotal moment highlights the essence of pragmatic innovation: recognizing and acting on opportunities to drive meaningful change. Kodak's reluctance to pivot to digital imaging preempted its decline, costing them dearly in an era of rapid technological advancement."

Fundamental Principles of Pragmatic Innovation

These principles serve as foundational guidelines for organizations seeking to drive meaningful and sustainable innovation in today's dynamic business environment.

Alignment with Business Goals: Pragmatic innovation should always be closely aligned with the overarching business objectives and strategies.

• Problem-Centric Approach: It focuses on systematically solving specific business problems or challenges rather than merely adopting new technologies for the sake of innovation.
• Integration of Technology and Business Strategy: It bridges the gap between technical solutions and business needs, ensuring that innovation efforts directly contribute to achieving strategic goals.
• Iterative and Incremental Progress: Pragmatic innovation often involves an iterative approach, where solutions are developed and refined incrementally based on feedback and evolving requirements.
• Flexibility and Adaptability: It emphasizes the ability to adapt to changing market conditions, customer preferences, and technological advancements while maintaining focus on the ultimate business objectives.
• Risk Management: It involves a balanced approach to risk-taking, where risks are assessed, mitigated, and managed effectively to minimize potential negative impacts on the business.
• Collaboration and Cross-functional Teams: It encourages collaboration across different departments and disciplines within an organization to leverage diverse perspectives and expertise in problem-solving.
• Customer-Centricity: Pragmatic innovation prioritizes understanding and addressing the needs, preferences, and pain points of customers to deliver value and drive business growth.
• Data-Driven Decision Making: It relies on data and insights to inform decision-making processes, enabling organizations to make informed choices and optimize outcomes.
• Continuous Learning and Improvement: It fosters a culture of continuous learning, experimentation, and improvement, where successes and failures are viewed as opportunities for growth and refinement.

Looking through the lens of data-driven decisions: Pragmatic Innovation Data Science Framework

In our journey to explore pragmatic innovation, particularly through the lens of Kodak's transformation and the broader implications for businesses today, we present a pragmatic innovation data science framework. Pragmatic innovation emphasizes practical, real-world solutions that deliver tangible value to the organization. This framework serves as a guide for leveraging data science and AI to drive innovation, aligning closely with strategic business objectives and market demands. The value of this framework lies in its coverage of the data science project lifecycle, from defining specific, measurable, relevant, and impactful business problems to setting strategic data science goals, and from data collection through to preparation, analysis, and deployment.

Other excellent frameworks exist for data science offering similar, iterative approaches to move through the stages of a data science project. This framework, however, focuses on the integration of data science into business processes and strategic objectives. This includes a clear emphasis on continuous improvement and the iterative refinement of data science projects based on feedback and impact assessment, thereby supporting sustainable, data-driven innovation.

Without a framework, there's a risk of ambiguity or misunderstanding about the problem being addressed. By following a framework, organizations can assess potential risks associated with the data science project and develop strategies to mitigate them. This helps in reducing uncertainties and ensuring smoother project execution. A well-defined framework promotes reproducibility and scalability of data science solutions. It establishes standard processes and methodologies that can be reused across different projects, leading to more consistent and reliable results. Frameworks often include mechanisms for feedback and iteration, allowing organizations to continuously improve their data science capabilities. This iterative approach fosters learning and innovation, driving long-term success.

Below, we outline the steps of our framework, each of which will be detailed in this blog series. We will apply the framework to the Kodak use case throughout the blog series, demonstrating how the organization could have used data science to support pragmatic innovation and change the trajectory of market decline.

Step	Description	Questions to Answer	Article
1. Define the Business Problem	Identify and articulate the core challenges the organization faces, with an emphasis on specificity, measurability, relevance, and impact.	What is the primary challenge facing Kodak in the digital age? What are the consequences of not addressing this issue?	Defining Business Problems: A Prerequisite for Innovation
2. Setting Data Science Goals	Establish clear, strategic objectives for data science initiatives that align with the business problems identified.	How can data science help Kodak address its business problem? What specific outcomes are expected from the data science initiative?	Data Science Goals: Aligning Innovation with Business Objectives
3. Data Collection	Gather relevant, trustworthy, and high-quality data that is essential for addressing the defined data science goals.	What data sources are available to Kodak? Are there any additional data sources that need to be accessed?	Data Collection for Pragmatic Innovation: Laying the Groundwork
4. Data Preparation	Process the collected data to ensure it is clean, organized, and ready for analysis.	Is the collected data clean and organized? What preprocessing steps are required to make the data analyzable?	Data Preparation: The Critical First Step in Data Analysis"
5. Choosing Analytical Methods	Select appropriate analytical techniques that match the data science goals and available data.	Which algorithms or analytical methods best address the data science goal? What tools and software will be used for analysis?	Choosing Analytical Methods
6. Model Building & Evaluation	Develop and assess models to ensure they effectively meet the data science goals.	How well does the model perform against predefined metrics? Are there any areas where the model needs improvement?	Coming Soon
7. Deployment	Implement the models and insights derived from them into practical business strategies and operations	How will the models be integrated into Kodak's existing systems? Who will use the models, and how will they access them?	Deployment: Turning Kodak’s Data Science Insights into Market Success
8. Monitoring & Maintenance	Continuously oversee and update the deployed solutions to maintain and enhance their effectiveness.	What mechanisms will be in place to monitor model performance? How frequently will models be updated or retrained?	Coming Soon
9. Impact Assessment	Evaluate the outcomes of the data science initiatives against the original business problems and goals.	How has the data science initiative affected Kodak's market share and revenue? What ROI has been achieved from the project?	Coming Soon
10. Feedback Loop	Incorporate feedback from all stages of the framework to refine and improve the process for future iterations.	What lessons have been learned from the implementation of data-driven strategies? How can the data science process be improved for future projects?	Coming Soon

The Kodak Lessons are relevant today: Adapting to the Digital Age

While we can't change the past, Kodak's story provides invaluable insights for the future. It underscores the importance of embracing innovation, leveraging data science, and the potential of AI/ML tools to foresee market shifts, enhance product development, and engage customers personally. As we look ahead, let Kodak's journey remind us always to be ready to adapt, innovate, and harness the power of technology to meet ever-changing market demands.

Conclusion: Capturing Tomorrow's Kodak Moments

As we embark on this exploration of pragmatic innovation through the Kodak use case, our goal is to inspire businesses to act with foresight and agility. By integrating the rigor of a data-driven approach with the flexibility and creativity of innovation, there's an opportunity to rewrite your company's future, capturing the Kodak moments of tomorrow before they slip away.

In our next post, we'll dive deeper into the first step of the innovation process: defining the business problem, setting the stage for a journey of discovery and transformation.